This focus session addresses a critical gap in pre-clerkship medical education: the development of authentic self-assessment capabilities that extend beyond traditional practice questions. Current evidence demonstrates significant limitations in medical student self-assessment, with meta-analyses showing only modest correlations (r = 0.042-0.334) between student self-assessment and actual performance. This session introduces an evidence-based approach using learning objectives as metacognitive scaffolds, combined with AI-supported prompting strategies (TRACI framework: Task-Role-Audience-Create-Intent) to develop reflective practitioners rather than rote memorizers.

Participants will experience hands-on implementation of structured self-assessment processes, learn validated evaluation methods using instruments like the Self-Reflection and Insight Scale (SRIS) and Metacognitive Awareness Inventory (MAI), and develop institutional implementation strategies based on the Consolidated Framework for Implementation Research (CFIR). Expected outcomes include practical tools for immediate implementation, evidence-based evaluation frameworks, and strategies for addressing technology integration challenges in medical education contexts.

Learning Outcomes

1. Apply evidence-based rationale for metacognitive scaffolding approaches in medical education, distinguishing between assessment accuracy and self-regulated learning skill development.
2. Design and implement AI-supported self-assessment processes using learning objectives as frameworks, incorporating the TRACI prompting methodology (Task-Role-Audience-Create-Intent) for reflective rather than memorization-based learning.
3. Select and utilize validated measurement instruments (SRIS, MAI, SRLPS) to evaluate metacognitive skill development and self-regulated learning outcomes in pre-clerkship students.
4. Create evaluation strategies that measure both immediate learning outcomes and long-term metacognitive skill transfer.